{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anthony Dave\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "home_folder = os.path.expanduser(\"~\")\n",
    "print(home_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ionosphere.data\n"
     ]
    }
   ],
   "source": [
    "# Change this to the location of your dataset\n",
    "data_filename = \"ionosphere.data\"\n",
    "print(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "# Size taken from the dataset and is known already\n",
    "X = np.zeros((351, 34), dtype='float')\n",
    "y = np.zeros((351,), dtype='bool')\n",
    "\n",
    "with open(data_filename, 'r') as input_file:\n",
    "    reader = csv.reader(input_file)\n",
    "    for i, row in enumerate(reader):\n",
    "        # Get the data, converting each item to a float\n",
    "        data = [float(datum) for datum in row[:-1]]\n",
    "        # Set the appropriate row in our dataset\n",
    "        X[i] = data\n",
    "        # 1 if the class is 'g', 0 otherwise\n",
    "        y[i] = row[-1] == 'g'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '0', '0.84710', '0.13533', '0.73638', '-0.06151', '0.87873', '0.08260', '0.88928', '-0.09139']\n",
      "g\n",
      "[[ 1.       0.       0.99539 -0.05889  0.85243  0.02306  0.83398 -0.37708\n",
      "   1.       0.0376   0.85243 -0.17755  0.59755 -0.44945  0.60536 -0.38223\n",
      "   0.84356 -0.38542  0.58212 -0.32192  0.56971 -0.29674  0.36946 -0.47357\n",
      "   0.56811 -0.51171  0.41078 -0.46168  0.21266 -0.3409   0.42267 -0.54487\n",
      "   0.18641 -0.453  ]\n",
      " [ 1.       0.       1.      -0.18829  0.93035 -0.36156 -0.10868 -0.93597\n",
      "   1.      -0.04549  0.50874 -0.67743  0.34432 -0.69707 -0.51685 -0.97515\n",
      "   0.05499 -0.62237  0.33109 -1.      -0.13151 -0.453   -0.18056 -0.35734\n",
      "  -0.20332 -0.26569 -0.20468 -0.18401 -0.1904  -0.11593 -0.16626 -0.06288\n",
      "  -0.13738 -0.02447]\n",
      " [ 1.       0.       1.      -0.03365  1.       0.00485  1.      -0.12062\n",
      "   0.88965  0.01198  0.73082  0.05346  0.85443  0.00827  0.54591  0.00299\n",
      "   0.83775 -0.13644  0.75535 -0.0854   0.70887 -0.27502  0.43385 -0.12062\n",
      "   0.57528 -0.4022   0.58984 -0.22145  0.431   -0.17365  0.60436 -0.2418\n",
      "   0.56045 -0.38238]]\n",
      "[ True False  True]\n"
     ]
    }
   ],
   "source": [
    "print(row[:10])\n",
    "print(row[-1])\n",
    "print(X[:3])\n",
    "print(y[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 263 samples in the training dataset\n",
      "There are 88 samples in the testing dataset\n",
      "Each sample has 34 features\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\n",
    "print(\"There are {} samples in the training dataset\".format(X_train.shape[0]))\n",
    "print(\"There are {} samples in the testing dataset\".format(X_test.shape[0]))\n",
    "print(\"Each sample has {} features\".format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "estimator = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 86.4%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = estimator.predict(X_test)\n",
    "accuracy = np.mean(y_test == y_predicted) * 100\n",
    "print(\"The accuracy is {0:.1f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy is 82.6%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "average_accuracy = np.mean(scores) * 100\n",
    "print(\"The average accuracy is {0:.1f}%\".format(average_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_scores = []\n",
    "all_scores = []\n",
    "temp_scores = []\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "# for n_neighbors in parameter_values:\n",
    "#     for i in range(10):\n",
    "#         estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "#         scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "#         temp_scores.append(scores)\n",
    "#     avg_scores.append(np.mean(temp_scores))\n",
    "#     all_scores.append(temp_scores)\n",
    "for n_neighbors in parameter_values:\n",
    "    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "    avg_scores.append(np.mean(scores))\n",
    "    all_scores.append(scores)\n",
    "# from matplotlib import pyplot as plt\n",
    "# plt.figure(figsize=(32,20))\n",
    "# plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "# #plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(list(range(1,21)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e481413850>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(32,20))\n",
    "plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "#plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for parameter, scores in zip(parameter_values, all_scores):\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e481463280>,\n",
       " <matplotlib.lines.Line2D at 0x1e4814632b0>,\n",
       " <matplotlib.lines.Line2D at 0x1e481463340>,\n",
       " <matplotlib.lines.Line2D at 0x1e481463430>,\n",
       " <matplotlib.lines.Line2D at 0x1e4814634f0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, all_scores, 'bx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "all_scores = defaultdict(list)\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    for i in range(100):\n",
    "        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\n",
    "        all_scores[n_neighbors].append(scores)\n",
    "for parameter in parameter_values:\n",
    "    scores = all_scores[parameter]\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e4808fc430>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, avg_scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.       0.       0.99539 -0.05889  0.85243  0.02306  0.83398 -0.37708\n",
      "   1.       0.0376   0.85243 -0.17755  0.59755 -0.44945  0.60536 -0.38223\n",
      "   0.84356 -0.38542  0.58212 -0.32192  0.56971 -0.29674  0.36946 -0.47357\n",
      "   0.56811 -0.51171  0.41078 -0.46168  0.21266 -0.3409   0.42267 -0.54487\n",
      "   0.18641 -0.453  ]\n",
      " [ 1.       0.       1.      -0.18829  0.93035 -0.36156 -0.10868 -0.93597\n",
      "   1.      -0.04549  0.50874 -0.67743  0.34432 -0.69707 -0.51685 -0.97515\n",
      "   0.05499 -0.62237  0.33109 -1.      -0.13151 -0.453   -0.18056 -0.35734\n",
      "  -0.20332 -0.26569 -0.20468 -0.18401 -0.1904  -0.11593 -0.16626 -0.06288\n",
      "  -0.13738 -0.02447]]\n"
     ]
    }
   ],
   "source": [
    "X_broken = np.array(X)\n",
    "print(X_broken[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.1       0.        0.099539 -0.05889   0.085243  0.02306   0.083398\n",
      "  -0.37708   0.1       0.0376    0.085243 -0.17755   0.059755 -0.44945\n",
      "   0.060536 -0.38223   0.084356 -0.38542   0.058212 -0.32192   0.056971\n",
      "  -0.29674   0.036946 -0.47357   0.056811 -0.51171   0.041078 -0.46168\n",
      "   0.021266 -0.3409    0.042267 -0.54487   0.018641 -0.453   ]\n",
      " [ 0.1       0.        0.1      -0.18829   0.093035 -0.36156  -0.010868\n",
      "  -0.93597   0.1      -0.04549   0.050874 -0.67743   0.034432 -0.69707\n",
      "  -0.051685 -0.97515   0.005499 -0.62237   0.033109 -1.       -0.013151\n",
      "  -0.453    -0.018056 -0.35734  -0.020332 -0.26569  -0.020468 -0.18401\n",
      "  -0.01904  -0.11593  -0.016626 -0.06288  -0.013738 -0.02447 ]]\n"
     ]
    }
   ],
   "source": [
    "X_broken[:,::2] /= 10\n",
    "print(X_broken[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The original average accuracy for is 82.6%\n",
      "The 'broken' average accuracy for is 73.8%\n"
     ]
    }
   ],
   "source": [
    "estimator = KNeighborsClassifier()\n",
    "original_scores = cross_val_score(estimator, X, y,\n",
    "  scoring='accuracy')\n",
    "print(\"The original average accuracy for is {0:.1f}%\".format(np.mean(original_scores) * 100))\n",
    "broken_scores = cross_val_score(estimator, X_broken, y,\n",
    "  scoring='accuracy')\n",
    "print(\"The 'broken' average accuracy for is {0:.1f}%\".format(np.mean(broken_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Binarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_transformed = MinMaxScaler().fit_transform(X)\n",
    "# X_transformed = StandardScaler().fit_transform(X)\n",
    "X_transformed = Binarizer().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy is 84.6%\n"
     ]
    }
   ],
   "source": [
    "# X_transformed = MinMaxScaler().fit_transform(X_broken)\n",
    "# X_transformed = StandardScaler().fit_transform(X_broken)\n",
    "X_transformed = Binarizer().fit_transform(X_broken)\n",
    "estimator = KNeighborsClassifier()\n",
    "transformed_scores = cross_val_score(estimator, X_transformed, y, \n",
    "  scoring='accuracy')\n",
    "print(\"The average accuracy is {0:.1f}%\".format(np.mean(transformed_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling_pipeline = Pipeline([('scale', MinMaxScaler()),\n",
    "#                              ('predict', KNeighborsClassifier())])\n",
    "scaling_pipeline = Pipeline([('scale', Binarizer()),\n",
    "                             ('predict', KNeighborsClassifier())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pipeline scored an average accuracy for is 84.6%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(scaling_pipeline, X_broken, y, scoring='accuracy')\n",
    "print(\"The pipeline scored an average accuracy for is {0:.1f}%\".format(np.mean(transformed_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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